from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for part_id in partition_order:
								__lowercase					     =				df.where(F"SPARK_PARTITION_ID() = {part_id}"				).collect()
								for row_idx, row in enumerate(lowercase				):
												expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict())				)
				return expected_row_ids_and_row_dicts





@require_not_windows
@require_dill_gt_0_3_2
def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
				__lowercase					     =				spark.range(100				).repartition(1				)
				__lowercase					     =				Spark(lowercase				)
				# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
				# that each partition can hold 2 rows.
				spark_builder._repartition_df_if_needed(max_shard_size=16				)
				# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
				assert spark_builder.df.rdd.getNumPartitions() == 50





@require_not_windows
@require_dill_gt_0_3_2
def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
				__lowercase					     =				spark.range(10				).repartition(2				)
				__lowercase					     =				[1, 0]
				__lowercase					     =				_generate_iterable_examples(lowercase				,     lowercase				)  # Reverse the partitions.
				__lowercase					     =				_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase				,     lowercase				)

				for i, (row_id, row_dict) in enumerate(generate_fn()				):
								__lowercase     ,	__lowercase					     =				expected_row_ids_and_row_dicts[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
				__lowercase					     =				spark.range(10				).repartition(1				)
				__lowercase					     =				SparkExamplesIterable(lowercase				)
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(lowercase				):
								assert row_id == F"0_{i}"
								assert row_dict == {"id": i}





@require_not_windows
@require_dill_gt_0_3_2
def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
				__lowercase					     =				spark.range(30				).repartition(3				)
				# Mock the generator so that shuffle reverses the partition indices.
				with patch('''numpy.random.Generator'''				) as generator_mock:
								__lowercase					     =				lambda lowercase				: x.reverse()
								__lowercase					     =				_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase				,     [2, 1, 0]				)

								__lowercase					     =				SparkExamplesIterable(lowercase				).shuffle_data_sources(lowercase				)
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(lowercase				):
												__lowercase     ,	__lowercase					     =				expected_row_ids_and_row_dicts[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
				__lowercase					     =				spark.range(20				).repartition(4				)

				# Partitions 0 and 2
				__lowercase					     =				SparkExamplesIterable(lowercase				).shard_data_sources(worker_id=0				,     num_workers=2				)
				assert shard_it_a.n_shards == 2
				__lowercase					     =				_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase				,     [0, 2]				)
				for i, (row_id, row_dict) in enumerate(lowercase				):
								__lowercase     ,	__lowercase					     =				expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict

				# Partitions 1 and 3
				__lowercase					     =				SparkExamplesIterable(lowercase				).shard_data_sources(worker_id=1				,     num_workers=2				)
				assert shard_it_a.n_shards == 2
				__lowercase					     =				_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase				,     [1, 3]				)
				for i, (row_id, row_dict) in enumerate(lowercase				):
								__lowercase     ,	__lowercase					     =				expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
				__lowercase					     =				spark.range(100				).repartition(1				)
				__lowercase					     =				Spark(lowercase				)
				# Choose a small max_shard_size for maximum partitioning.
				spark_builder._repartition_df_if_needed(max_shard_size=1				)
				# The new number of partitions should not be greater than the number of rows.
				assert spark_builder.df.rdd.getNumPartitions() == 100
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)